Systems and methods for evaluating and mitigating problem behavior by detecting precursors

ABSTRACT

Systems and methods for predicting problem behavior in individuals with developmental and behavior disabilities. A plurality of sensors are configured to collect multimodal data signals of a subject individual including a wearable upper body motion sensing device with a plurality of inertial measurement units (IMUs). An electronic controller is configured to receive output signals from each of IMUs and to model an upper body position of the subject individual based on the output signals from the IMUs. A trained machine-learning model is then applied by providing an input data set that includes multimodal signal data (e.g., including signal data from at least one IMU) and/or features extracted from the multimodal signal data. The machine-learning model is trained to produce as output an indication of whether a precursor to the problem behavior is detected and, in response to detecting the precursor, a notification (or alarm) is generated.

RELATED APPLICATIONS

This application claims the priority benefit of U.S. Provisional PatentApplication No. 63/217,585, filed Jul. 1, 2021, entitled “PORTABLEAPPLICATION TO RECORD GROUNDTRUTH OF AFFECTIVE STATES AND MULTIMODALDATA COLLECTION SYSTEM TO PREDICT IMMINENT PRECURSOR OF PROBLEMATICBEHAVIORS,” the entire contents of which are hereby incorporated hereinby reference.

BACKGROUND

The present invention relates to systems and methods for evaluating andtreating individuals (e.g., children and adolescents) that may exhibitproblem behaviors including, for example, individuals with developmentaldisabilities and/or autism spectrum disorder (ASD).

SUMMARY

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder thataffects 1 in every 59 children in the United States. Over two thirds ofchildren with ASD display frequent problem behaviors (PB) that mayentail physical harm to self or others, non-compliance, propertydestruction, and elopement. Although common in ASD, these behaviors arenot limited to autism diagnoses. They also occur as part of otherdevelopmental or genetic disorders, such as intellectual disability, aswell as psychiatric disorders. Precursors (PC) are signs that a PB maybe imminent. If a parent or caregiver can observe the precursors of PBin time to intervene, some PB can be mitigated or prevented.

In some implementations, the systems and methods described hereinprovide a multimodal data capture system that gathers multi-sensory datafrom an individual and utilizes a trained machine-learning model todetect precursors of problem behavior based on data signals collectedfrom a plurality of sensors and/or features extracted from the datasignals. Data channels may include, for example, motion (e.g.,accelerations, joint angles, gesture patterns, pose, etc.), audio (e.g.,pitch, volume, speech, words, etc.), physiological signals (e.g., heartrate, body temperature, skin conductance, bio-impedance, etc.), facialexpressions (e.g., happy, mouth open, eyes open, etc.), and headrotations/position.

In some implementations, the machine-learning model is trainedspecifically for an individual based on observed occurrences of problembehavior & precursors as well as contemporaneously captured datasignals. In other implementations, a group or collective model istrained for use by a plurality of different users based on observed dataand captured data signals. In some implementations, the system mayinclude a self-monitoring application (e.g., implemented on a portabledevice such as a smart phone) configured to collect the data signals andapply the trained machine-learning model. In some implementations, theapplication may be configured to generate an output notification (e.g.,text, audio, visual, etc.) configured to warn the user that he/she isshowing precursor signs of problem behavior that warrant attention. Insome implementations, the system may be configured to providerecommendations for mitigation (e.g., take deep breaths, temporarilystep away from situation, etc.). In some implementations, otherindividuals (e.g., parents, caregivers, therapists, educators, etc.) mayalso receive a notification of the detected precursor.

In some implementations, the systems and methods described hereinprovide a multi-modal data collection system on precursors of problembehaviors that include multiple channels of data signals. In someimplementations, the system includes a wearable motion sensor systemthat is feasible to such populations (e.g., a “wearable intelligentnon-invasive gesture sensor” (WINGS) system for capturing motion signaldata).

In some implementations, the system is configured to further train(e.g., retrain) the machine-learning model based on user indications offalse positives and false negatives. For example, in someimplementations, in response to the machine-learning model indicatingthat a precursor has been detected based on the captured signals, thesystem may generate a prompt to a user (e.g., the individual beingmonitored, a caregiver, a parent, etc.) asking the user to confirmwhether a precursor actually occurred and/or whether a problem behaviorresulted. Conversely, in some implementations, the software applicationmay be configured to allow a user (e.g., the individual being monitored,a caregiver, a parent, etc.) to provide a timestamped user inputindicating that an undetected precursor and/or problem behavioroccurred. Based on the user feedback, the system is configured toidentify a set of captured data signals corresponding to the time of theuser input and to retrain the machine-learning model to associate theset of captured data signals with a precursor (in the event of a falsenegative) and/or to retrain the machine-learning model to no longerassociate the set of captured data signals with the precursor (in theevent of a false positive).

In some implementations, the systems and methods described hereinprovide a mechanism for training the machine-learning model based, forexample, on data signals collected during administration of anevaluation analysis session for the individual. For example, theinterview-informed synthesized contingency analysis (IISCA) is oneapproach used for the assessment of children and adolescents withproblem behaviors. The IISCA includes a controlled series ofexperimental trials in which the relation between a child's problembehavior and the environment is verified through the structuredpresentation of alternating preferred and then non-preferred situations.In some implementations, the system described herein provide asoftware-based application for use by a behavior analyst during an IISCAvia which the behavior analyst records behaviors and affective states ofthe individual undergoing the IISCA. For example, in someimplementations, the software application is configured to enable thebehavior analyst to indicate observed information such as (1)occurrences of problem behavior, (2) occurrences of precursors, and (3)confirmation that the subject is “calm.”

In some implementations, the subject individual is asked to wear themulti-modal data capture system while undergoing the IISCA (or otherbehavior analysis protocol). The collected data from the multi-modaldata capture system is timestamped and stored on a memory (e.g.,, alocal memory or a cloud storage system). Concurrently, information fromthe software application indicating observed occurrences of problembehavior, precursors, and calm demeanor are also timestamped and stored.The system is then configured to train the machine-learning model byassociating sets of data signals with the corresponding behavior state(e.g., which data signals are associated with precursors, which areassociated with problem behaviors, and which are associated with a calmstate).

In some implementations, the multi-modal data capture system and thetrained machine-learning model are configured to detect precursors toproblem behavior. In this manner, the system is configured todetect/predict that the conditions for problem behavior are present andthat, without intervention/mitigation, the problem behavior is likely tooccur imminently. However, the system is configured to detect theprecursors before the problem behavior actually occurs so that, in somecases, the problem behavior might be preventable.

In some implementations, the multi-modal data capture system includes awrist-worn device (configured to sense physiological signals), amicrophone (configured to detect words, sounds, patterns, etc.), and anon-invasive set of gesture sensors (e.g., inertial measurement units(IMUs) configured to capture data that can be used to reconstruct/modelthe upper body pose and movements (e.g., a child that begins to flailtheir arms around may be a precursor)). In some implementations, themicrophone is selectively attachable to a garment worn by the subjectindividual. In other implementations, the microphone and/or othersensors may be integrated into a piece of clothing worn by the subjectindividual. For example, in some implementations, an upper body garment(e.g., a shirt, a sweatshirt, a vest) is configured with a series ofpockets positioned at different locations. Each pocket is sized andpositioned to non-invasively hold an inertial measurement unit.Similarly, in some implementations, the microphone may be integratedinto the garment itself (e.g., in the hood of a hooded sweatshirt) sothat it does not need to be worn or attached to the subject individualseparately.

In one embodiment, the invention provides a system for predictingproblem behavior in individuals with developmental and behaviordisabilities. A wearable upper body motion sensing device includes aplurality of inertial measurement units (IMUs) positioned at knownpositions relative to each other. An electronic controller is configuredto receive output signals from each of IMUs and to model an upper bodyposition of the subject individual based on the output signals from theIMUs. A trained machine-learning model is then applied by providing aninput data set that includes signal data from at least one IMU, themodeled upper body position information, and/or a sequence of upper bodyposition information. The machine-learning model is trained to produceas output an indication of whether a precursor to the problem behavioris detected and, in response to detecting the precursor, a notification(or alarm) is generated.

In some implementations, the system is configured to receivingmultimodal data captured by the wearable upper body motion sensingdevice and other sensor systems including, for example, physiologicaldata, audio data, and/or video data. In some implementations, the inputdata set further includes data signals from one or more of the othersensors systems and/or features extracted from the data signals from theone or more of the other sensor systems.

In another embodiment, the invention provides a method of training themachine-learning model to be used to detect precursors to the problembehavior. The subject individual is equipped with the wearable upperbody motion sensing device (and, in some implementations, one or moreadditional sensor systems to generate multimodal training data). Abehavior assessment (e.g., the IISCA) is administered to the subjectindividual while recording signal data from the plurality of inertialmeasurement units. Concurrently, user inputs are received through acomputer-based user device while the behavior assessment isadministered. The user inputs provide real-time indications of observedoccurrences of the problem behavior and observed occurrences of theprecursor. A set of training data is then generated by defining aplurality of ground truth labels based on the received real-timeindications of observed occurrences of the problem behavior and theobserved occurrences of the precursor. Each real-time indication ismapped to recorded signal data form the plurality of inertialmeasurement units. The machine-learning model is then trained, based onthe set of training data, to produce an output corresponding to theground truth label in response to receiving as input the signal datamapped to the ground truth label, upper body position informationdetermined based on the signal data mapped to the ground truth label,and/or a sequence of upper body position information determined based onthe signal data mapped to the ground truth label.

Other aspects of the invention will become apparent by consideration ofthe detailed description and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart of a method for training a machine-learning modelto detect precursors of problem behavior in a subject individual (e.g.,an individual with developmental disabilities (IDD)).

FIG. 2 is a flowchart of a method for applying the trainedmachine-learning model of FIG. 1 to detect precursors of problembehavior based on data signals captured by a multi-modal data capturesystem.

FIG. 3 is a block diagram of the multi-modal data capture system used inthe method of FIG. 2 according to one implementation.

FIG. 4 is a block diagram of a wearable non-invasive gesture sensorsystem according to one implementations.

FIGS. 5A, 5B, and 5C are different perspective views of a wearablegarment configured to hold the inertial measurement units of the systemof FIG. 4 during use.

FIG. 6 is an example of a graphical model of pose and movement of asubject individual recreated based on data signals captured by theinertial measurement units of the system of FIG. 4 .

FIG. 7A is another example of a graphical model of a pose of a subjectindividual.

FIG. 7B is a perspective view of the subject individual posed in theposition corresponding to the graphical model of FIG. 7A.

FIG. 8 is a screenshot of a graphical user interface of a software-basedapplication for recording behavior observations during an IISCA.

FIG. 9 is a state-diagram of the operation of the software-basedapplication of FIG. 8 .

DETAILED DESCRIPTION

Before any embodiments of the invention are explained in detail, it isto be understood that the invention is not limited in its application tothe details of construction and the arrangement of components set forthin the following description or illustrated in the following drawings.The invention is capable of other embodiments and of being practiced orof being carried out in various ways.

FIG. 1 illustrates an example of a framework for developing amachine-learning model trained to associate multi-modal signal data withprecursors to problem behavior as a mechanism for anticipating problembehaviors before they occur. In the example of FIG. 1 , training datafor the machine-learning model is generated in the context of anInterview Informed Synthesized Contingency Analysis (IISCA). IISCA is atype of practical functional assessment (PFA). IN this assessment, abehavior analyst methodically manipulates the environment to testcaregiver hypotheses around what environmental stimuli serve asantecedents or establishing operations (EOs) to a problem behavior, andwhich stimuli serve as reinforcers to the problem behavior. During theassessment, the subject (e.g., IDD) interacts with a behavior analystand/or therapist and receives behavioral reinforcement.

During the IISCA session (step 101), a multi-modal data capture systemoperates to collect multi-modal data indicative of the subject'sresponse to stimuli (step 103). As described in further detail below, insome implementations, the multi-modal data capture system is configuredto capture data such as, for example, physiological signals, motionsignals, pose signals, audio signals, and/or facial expression data. Thecaptured multi-modal data is then processed for feature selection (step105). For example, captured video data may be processed toidentify/categorize facial expressions by type, the physiologicalsignals may be processed to quantify a heart rate, the motion signalsmay be processed to determine a pose and/or body gesture of the subject,and the audio signal may be processed to identify changes in pitchand/or specific words used by the subject.

While the multi-modal data capture system operates to collect themulti-modal data, the behavior analyst observes the subject and, using asoftware-based application (as described in further detail below) (step107), records observations regarding the behavioral state of the subjectincluding, for example, when problem behaviors are observed, whenprecursors are observed, and when the subject appears calm (step 107).These recorded behavioral states are timestamped and then associatedwith features extracted from the multi-modal data to form the set oftraining data. The observed behavioral states serve as the “groundtruth” (step 109) for the temporally corresponding multi-modal dataand/or extracted features while training the machine learning model(step 111). The machine-learning model is trained by mapping thefeatures extracted form the multi-modal data against the ground truth sothat, in response to receiving a set of input data (including, forexample, the multi-modal data and/or features extracted from themulti-modal data), the machine-learning model produces an outputindicative of whether a precursor behavior is detected which wouldindicate that an occurrence of problem behavior is imminent if notmitigated.

The example of FIG. 1 (and other examples discussed herein below)describe training the machine-learning model in the context of an IISCAsession. However, in other implementations, other types of behaviorassessment protocols may be used to generate ground truth behavioralstate observations while multi-modal data is captured. Furthermore, insome implementations, the system is configured to train themachine-learning model specifically to map extracted features to theground truth behavioral state while, in other implementations, thesystem may be configured to train the machine-learning model based onthe (raw or filtered) multi-modal data signals themselves and/or acombination of extracted features and signals from the set ofmulti-modal data signals. Similarly, in some implementations, themachine-learning model is trained to map features/signals specificallyto observed precursors that are flagged by the behavior analyst as“precursors.” However, in other implementations, the system isadditionally or alternatively configured to respond to observedoccurrences of problem behavior by analyzing the features/signalspreceding the occurrence of the problem behavior to attempt to discernsignals, features, or combinations thereof that can be indicative of a“precursor” to the problem behavior—even if the subject did not exhibitany observable “precursor” behavior. In this manner, themachine-learning model can be trained to detect precursors to problembehavior that may not be visually observable by a behavior analyst.

FIG. 2 illustrates an example of a method, executed by a computer-basedsystem and the multi-modal data capture system, for detecting precursorsand predicting imminent problem behavior in a subject based on thetrained machine-learning model. The multi-modal data capture systemcollects multi-modal data streams (step 201) and sets of the multi-modaldata and/or features extracted from the multi-modal data are provided asinput to the trained machine-learning model (step 203). If the output ofthe machine-learning model indicates that a precursor is detected (step205), the system generates an output notification and/or mitigation toattempt to prevent the problem behavior from occurring (step 207).

In some implementations, the system is configured to respond to adetected precursor by generating an audio and/or visual notification,for example, on a device such as a smart phone, tablet computer, orcomputer workstation. In some implementations, the system can beconfigured to generate the notification on the devices associated withone or more different users. For example, in some implementations, thesystem is configured to generate the notification on devices associatedwith the subject/patient, the behavior analyst/therapist, a parent, acaregiver, and/or an educator. In some implementations, the system maybe configurable to allow the user to indicate which users are to receivethe notifications.

Additionally or alternatively, in some implementations, the system maybe configured to initiate an action to attempt to mitigate the problembehavior (i.e., the prevent the problem behavior from occurring). Forexample, the system may be configured to automatically launch a softwareapplication on the portable device of the subject/patient initiatingbreathing exercises to attempt to calm the subject/patient. In otherimplementations, the system may be configured to include a recommendedaction in the notification displayed to the user. For example, thesystem may generate a notification indicating that a precursor has beendetected and recommending that the subject step away from the situationfor a period of time to calm down. In still other implementations, thesystem may be configured to initiate some form of actuators to performan automated system task in response to detecting the precursor.Finally, in some implementations, the system may be configured toperform the same notification/mitigation whenever a precursor isdetected. However, in other implementations, the machine-learning modelmay be trained to identify a specific type or category of precursor andthe system may be configured to perform a different type ofnotification/task based on the type/category of the detected precursor.

Returning to the example of FIG. 2 , in some implementations, the systemis configured to generate updated training data based on user-providedfeedback regarding occurrences of false positives and/or falsenegatives. For example, if the system generates a notificationindicating that a precursor has been detected, but a user observing thesubject (e.g., a parent, caregiver, or the subject themselves) disagreeswith the assessment or, for example, if no problem behavior occurs afterthe machine-learning model detects the alleged precursor, the user mayprovide a user input via a user interface (e.g., of a softwareapplication) indicating a false positive. Conversely, if a problembehavior is observed in the subject without the machine-learning modelfirst detecting a precursor, the user may provide a user input via theuser interface indicating a false negative. In response to receiving oneor more user inputs indicative of false positives or false negatives(step 209), the system will retrain the machine-learning model (step211) based on the captured multi-modal data and/or the extractedfeatures using the user input as the ground truth for the new trainingdata. In some implementations, this retraining is performed periodicallyafter expiration of a time interval while, in some implementations, themachine-learning model is retrained in response to capturing a thresholdamount of new training data (e.g., a threshold number of false positivesand/or false negatives). In some implementations, the retraining isperformed when the multi-modal data capture system is not in use.

FIG. 3 illustrates an example of a multi-modal data capture system. Thesystem of FIG. 3 integrates and synchronizes multiple data modalities ofdifferent time scales. The system includes one or more video/camerabased systems 301, a system of wearable motion sensors 303, one or moreaudio sensors 305, and one or more physiological sensors 307. In someimplementations, the data captured by the video/camera-based system 301is processed to extract features such as, for example, head pose, facialexpression, and head motion. In some implementations, the data capturedby the system of wearable motion sensors 303 is processed to extractfeatures such as, for example, body movements and body pose. In someimplementations, the data captured by the audio sensor 305 is processedto extract features such as, for example, individual words and phrases,changes in verbal pitch, and changes in volume of the subject's voice.In some implementations, data captured by the physiological sensor 307is processed to extract features such as, for example, bio-impedance,heart rate, skin conductance, and body temperature.

In the example of FIG. 3 , the sensor systems are communicativelycoupled to a data collection computer system 309. The data collectioncomputer system 309 includes an electronic processor 311 and anon-transitory, computer-readable memory 313. The memory 313 stores dataand computer-executable instructions that, when executed by theelectronic processor 311, provide the functionality of the datacollection computer system 309—including, for example, the functionalitydescribed herein. In some implementations, the data signals collected bythe various sensor systems are transmitted to the data collectioncomputer system 309 and stored to the memory 313. In someimplementations, the data collection computer system 309 receives thecollected multi-modal data signals and stores the data in acloud-computing environment. In some implementations, the datacollection computer system 309 is configured to analyze the collecteddata signals and to perform feature extraction. Similarly, in someimplementations, the data collection computer system 309 is configuredto apply the trained machine-learning model by providing a set of inputdata (including, for example, some or all of the collected data signalsand/or features extracted from the data signals) as input to the trainedmachine-learning model and then taking appropriate action (e.g.,generating notifications to the appropriate devices) in response to themachine-learning model generating an output indicative of a detectedprecursor.

The multi-modal data collection system of FIG. 3 is only one example. Inother implementations, the multi-modal data collection system mayinclude other sensor systems in addition to or instead of thoseillustrated in the example of FIG. 3 . Conversely, in someimplementations, the multi-modal data collection system may include onlysome, but not all of the sensor systems illustrated in the example ofFIG. 3 . For example, in some implementations, the multi-modal datacollection system may include a set of wearable motion sensors 303, anaudio sensor 305, and a physiological sensor 307, but does not includeor utilize video/camera data. Similarly, the example of FIG. 3illustrates a set of examples of types of features that may be extractedfrom the captured multi-modal data. However, in other implementations,the system may be configured to extract more, fewer, and/or differentfeatures from the multi-modal data.

In some implementations, the video/camera-based system 301 includes astereoscopic camera system such as, for example, a Microsoft Kinect V2device. In the example of FIG. 3 , the Microsoft Kinect V2 device isconfigured to detect the facial expressions and head rotations of thesubject. The Microsoft Kinect API computes positions of eyes, nose andmouth among different points on the face from its color camera and depthsensor to recognize facial expressions and compute head rotations. Insome implementations, the API is integrated with the software executedby the data collection computer system 309 to read these measurements inC# scripts. In some such implementations, the system is designed totrack a first subject that enters the field of view of the Kinect deviceand detected facial expressions are assigned to one of a plurality ofdifferent predetermined categories including, for example, “happy,”“eyes closed,” “mouth open,” “looking away,” and “engaged.” All themeasures are computed in real-time and vary on a discrete numericalscale that ranges from 0, 0.5 and 1, meaning no, probably, and yes,respectively. The head rotations are measured in terms of roll, pitchand yaw angles of the head. The sampling rates of the head rotations andfacial expressions are both 10 Hz and the signals are recorded with timestamps with millisecond precision. The Kinect is placed on the wall by a3D printed structure which can adjust the pan and tilt angles of theKinect so that it directly faces the child during observation.

In some implementations, the physiological sensors 307 are implementedas a wrist-worn device configured with a plurality ofsensors/electrodes—for example, an E4 wristband. In someimplementations, the wristband itself is non-invasive and looks like asmart watch. The sampling rates used by the wrist-worn device to measureblood volume pulse (BVP) and electrodermal activity (EDA) are 64 Hz and4 Hz, respectively. In some implementations, the wrist-worn device alsoincludes an integrated contact temperatures sensor configured to measurebody temperature when the sensor is in contact with the skin surface ofthe subject. In some implementations, wrist-worn device includes anelectronic controller and a wireless transceiver. The electroniccontroller of the wrist-worn device is configured to record the senseddata with precise time stamps. The real-time physiological data streamis transferred to the data collection computer system 309 via thewireless transceiver.

In some implementations, the wearable motion sensors 303 are integratedinto a wearable garment and can thereby non-invasively measure bodymovements and poses when the garment is worn by the subject. FIGS. 4through 5C illustrates an example of a body movement tracking system(i.e., a “wearable intelligent non-invasive gesture sensor” or “WINGS”).The body movement tracking system is a portable, non-invasive tool formeasuring upper body motion. The body movement tracking systemintegrates a plurality of inertial measurement units (IMUs) into awearable garment such as, for example, a sweater, a shirt, or a vestthat resembles clothing that the subject is comfortable wearing. Toincrease the likelihood that the platform will be tolerated by childrenwith varying levels of activity, sensory sensitivity, and cognitivefunctioning, the device focuses on comfort, concealment, robustness, andsafety.

FIG. 4 illustrates an example of the electronic components of the bodymovement tracking system. A plurality of inertial measurement units(IMUs) 401.1, 401.2, 401.3, . . . 401.n are positioned at locationsalong the arms and back of the subject. Each IMU 401 is configured tomeasure acceleration and magnetic field in three-dimensions. Asdescribed in further detail below, the IMUs 401 are positioned atstrategic locations on the arms and torso of the subject such that thedata collection computer system 309 can determine relative position andangles of the body segments in order to generate a model of bodymovement and position based on the output signals from the plurality ofIMUs 401. In some implementations, the body movement tracking systemincludes a total of seven IMUs 401 to measure joint angles of eachforearm, upper arm, and the back. However, in other implementations, thebody movement tracking system may include more or fewer IMUs 401.

Each IMU is wiredly coupled to a data multiplexer 403 that is configuredto search and loop through the connected IMUs 401 and to periodicallycouple each IMU 401 to the data input channel of a motion sensor systemcontroller 405. The motion sensor system controller 405 includes anelectronic processor 407 and a non-transitory, computer-readable memory409. The motion sensor system controller 405 is configured to send themotion data via a wireless transmitter 411 to a wireless received of thedata collection computer system 309.

FIGS. 5A through 5B illustrate an example of a garment configured foruse with the body movement tracking system of FIG. 4 . The garment 501is a sweatshirt equipped with an upper arm pocket 503 and a forearmpocket 505 on each arm and at least one back pocket 507 on the back. Thepockets 503, 505, 507 are each sized to hold an individual IMU 401 and,in some implementations are selectively closable with hook-and-loopfasteners positioned along the opening of each pocket 503, 505, 507. Inother implementations, the pockets 503, 505, 507 are sewn shut. Thegarment 501 is designed such that the pockets 503, 505, 507 are discreteand do not stand out. The data multiplexer, the motion sensor systemcontroller 405, and the wireless transmitter 411 are also integratedinto the garment 501 (e.g., in a hood of a hooded sweatshirt or inanother pocket). In some implementations, a microphone (e.g., audiosensor 305) is also integrated into the garment 501 (for example, alongthe neck of the garment 501).

From the accelerometer readings (accl_(x), accl_(y) and accl_(z)) andthe magnetometer readings (mag_(x), mag_(y) and mag_(z)) output fromeach of the IMUs 401, the data collection computer system 309 is able tocompute the roll, pitch and yaw angles (θ, ψ, ϕ) of the torso and limbsaccording to equations (1), (2) and (3). The roll and pitch angles arecomputed by the IMU orientations with respect to the gravitationaldirection. The yaw angle can be computed by the relative IMUorientations with respect to the earth magnetic field.

$\begin{matrix}{\theta = {\tan^{- 1}\left( \frac{accl_{y}}{\sqrt{{accl_{y}^{2}} + {accl_{z}^{2}}}} \right)}} & (1)\end{matrix}$ $\begin{matrix}{\psi = {\tan^{- 1}\left( \frac{accl_{x}}{\sqrt{{accl}_{y}^{2} + {accl_{z}^{2}}}} \right)}} & (2)\end{matrix}$ $\begin{matrix}{\phi = {\tan^{- 1}\left( \frac{{mag_{z}s\psi} - {mag_{y}c\theta}}{{mag_{x}c\theta} + {mag_{y}s\theta s\psi} + {mag_{z}c\psi s\theta}} \right)}} & (3)\end{matrix}$

With the roll, pitch and yaw angles of different joints, data collectioncomputer system 309 then computes the 3D positions and orientations ofeach joint using forward kinematics. As shown in FIG. 6 , the base frameis set at the spine base of the subject. The base frame positivedirections of the x, y and z axes are front, left directions of thesubject and up, respectively. Then coordinate frame s_(n) is attached toeach body joint. Homogeneous transformation matrices H_(Joint n)^(Joint n-1) between the nth joint and the last joint consist of twoparts, a 3-by3 rotation matrix R_(n) ^(n-1) and a 1-by-3 translationvector d₀ ^(n-1). The rotation and translation matrices can align andmove the previous coordinate frame to the current coordinate frame,respectively. The rotation matrix is computed by roll, pitch and yawangles while the translation vector is computed by the body link lengthswhich are manually measured from different sizes of garments 501 (ormeasured separately for the specific individual subject). Eachhomogeneous transformation matrix is computed as equation (4). Then theoverall homogeneous transformation matrix H_(Joint n) ^(Origin) betweenthe base frame and the nth frame can be computed by multiplying all thehomogeneous transformation matrices as in equation (5). From thismatrix, d_(n) ⁰ can be read and that is the 3D position of the nth jointposition with respect to the base frame.

$\begin{matrix}{H_{{Jo}{int}n}^{{{Jo}{int}n} - 1} = {\begin{bmatrix}R_{n}^{n1} & d_{0}^{n - 1} \\\overset{\rightarrow}{0} & 1\end{bmatrix} = {\begin{bmatrix}{R_{x,\psi}R_{y,\theta}R_{z,\phi}} & d_{0}^{n - 1} \\\overset{\rightarrow}{0} & 1\end{bmatrix} = \left. \lbrack\begin{matrix}{c\phi_{n}c\theta_{n}} & {{c\phi_{n}s\theta_{n}s\psi_{n}} - {s\phi_{n}c\psi_{n}}} & {{s\phi_{n}s\psi_{n}} + {c\phi_{n}s\theta_{n}c\psi_{n}}} & x_{n}^{n - 1} \\{s\phi_{n}c\theta_{n}} & {{s\phi_{n}s\theta_{n}s\psi_{n}} + {c\phi_{n}c\psi_{n}}} & {{s\phi_{n}s\theta_{n}c\psi_{n}} - {c\phi_{n}s\psi_{n}}} & y_{n}^{n - 1} \\{{- s}\theta_{n}} & {c\theta_{n}s\psi_{n}} & {c\theta_{n}c\psi_{n}} & z_{n}^{n - 1} \\0 & 0 & 0 & 1\end{matrix} \right\rbrack}}} & (4)\end{matrix}$ $\begin{matrix}{H_{{Jo}{int}{}n}^{Origin} = {{H_{{Jo}{int}1}^{Origin}\square H_{{Jo}{int}2}^{{Jo}{int}1}\ldots H_{{Jo}{int}n}^{{{Jo}{int}{}n} - 1}} = {{\begin{bmatrix}R_{1}^{0} & d_{1}^{0} \\\overset{\rightarrow}{0} & 1\end{bmatrix}{\ldots\begin{bmatrix}R_{n}^{n‐1} & d_{n}^{n‐1} \\\overset{\rightarrow}{0} & 1\end{bmatrix}}} = \begin{bmatrix}R_{n}^{0} & d_{n}^{0} \\\overset{\rightarrow}{0} & 1\end{bmatrix}}}} & (5)\end{matrix}$

Thus, we have the 3D positions of each body joint and we can constructand visualize body gestures. In some implementations, a software program(e.g., a MATLAB program) is configured to visualize the upper bodygestures in real time. A comparison of a visualized gesture and acorresponding photograph of the subject is shown in FIGS. 7A and 7B. Thelines in FIG. 7A represent body limbs while the dots represent bodyjoints.

As described above in reference to FIG. 1 , the machine-learning modelthat is used to detect precursors is trained during performance of abehavioral assessment protocol such as, for example, the IISCA. TheIISCA usually requires observers to record the occurrence of precursorsof problem behaviors by paper and pen while timestamping events via astopwatch in order to record the conditions under which target behaviorswere observed. This method requires significant attention on the part ofthe behavioral analyst and is limited in precision with regard torecording the onset time of precursors.

FIG. 8 illustrates an example of a graphical user interface for asoftware application configured to be used by a behavioral analystobserving the IISCA. In particular, the software application is designedto be run on a portable device such as, for example, a tablet computer.The software application includes three different operating modes:Initialization, Session Information, and Summary. The graphical userinterface 801 illustrated in FIG. 8 is for use during the “SessionInformation” phase. In some implementations, other graphical userinterface pages are displayed during the Initialization and Summaryphases while, in other implementations, relevant information for theInitialization and Summary phases may be displayed in different windows(e.g., pop-up windows).

During the IISCA, there are two therapist-imposed conditions within theassessment protocol: establishing operations (EO) and reinforcingstimulus (SR). Establishing Operations (EO) represent those antecedentconditions reported to evoke behavioral escalation by care givers.Reinforcing Stimulus (SR) (or “Synthesized Reinforcement”) representintervals in which antecedent conditions are arranged to prevent,de-escalate, and restore a state of happy and relaxed engagement in thesubject.

The graphical user interface 801 for the Session phase includes severalbuttons relating to observer actions and observed subject behaviors. Aseries of buttons are provided to provide an interface for assessing theestablishing operations and the response of the subject. For example,the behavioral analysts presses the “Easy EO Demand” button 803 when an“easy” EO demand is initiated and presses the “Easy EO Comply” button805 when the subject complies with the EO demand. Similarly, a “Hard EODemand” button 807 is pressed with a more difficult/challenging EOdemand is initiated and the “Hard EO Comply” button 809 is pressed whenthe subject is observed as complying with the EO demand. The graphicaluser interface 801 also includes numeric fields 811, 813 displaying thenumber of EO conditions under which the subject has successfullycomplied without exhibiting the problem behavior. The graphical userinterface 801 also allows the behavioral analyst to toggle between thetwo conditions (i.e., move between establishing operations andreinforcing stimulus) by clicking the EO button 815 or the SR button 817(as described in further detail below).

The graphical user interface 801 also provides a series of buttons viawhich the behavior analyst can record observations of the behavioralstate of the subject. A “Problematic Behavior” button 821 is to bepressed when problem behavior is observed and a numeric field 823indicates the number of occurrences of the problem behavior during thecurrent IISCA session. Similarly, a “Pre-Cursor” button 825 is to bepressed when precursor behavior is observed and a corresponding numericfield 827 indicates the number of occurrences of precursor behaviorduring the current IISCA session. A “CALM” button 829 is to be pressedto indicate when the subject is observed to be in a “calm” behavioralstate (i.e., no problem or precursor behaviors).

Finally, a “NEXT SESSION” button 831 is provided to be pressed by theuser at the end of each IISCA session and an “END” button 833” isprovided to be pressed to when all IISCA sessions are completed for thesubject. As described further below, pressing the “NEXT SESSION” button831 causes the software application to generate and display summary datafor the individual IISCA session and pressing the “END” button 833causes the software application to generate and display summary data forall of the IISCA sessions and closes the “Session” graphical userinterface 801.

In some implementations, the software application operates as a finitestate machine (FSM) that captures the IISCA protocol as illustrated inFIG. 9 . The software application starts in the initialization state901. After entering the appropriate session and subject informationthrough the graphical user interface, the user presses a “Start” buttonand the software application proceeds to the Synthesized Reinforcement(SR) phase 905. The software application remains in the SR phase 905until the user presses the CALM button 829 indicating that the subjectis observed to be in a calm behavioral state. In response to thepressing of the CALM button 829, the software application moves to aticking phase 911 in which a timer monitors to make sure that the childis truly happy, relaxed, and engaged for at least 90 continuous seconds.After expiration of the 90 second timer, the software application movesto an EO Ready phase 913 in which the graphical user interface 801indicates that the establishing operations conditions may be applied. Insome implementations, this is indicated by displaying the EO Button 815in a different color (e.g., green when in the EO Ready phase 913 and redwhen in the Ticking phase 911).

If precursor behavior is observed (e.g., as indicated by a user pressingthe Pre-Cursor button 825) while in the ticking phase 911 or the EOready phase 913, the software application returns to the SR phase 905.Otherwise, if the user presses the EO button 815 while the softwareapplication is in the EO Ready phase 913, the software application movesinto the Establishing Operations (EO) phase 903. Upon pressing the EObutton 815, the behavior analyst and/or a therapist are to begin theapplicable Establishing Operation. While operating in the EO phase 903,if the precursor button 825 is pressed, a precursor event is recorded(with a timestamp) and the software application moves into a secondticking phase 907 in which a 30 second countdown is provided before thesoftware application moves into an SR Ready phase 909 and provides anindication via the graphical user interface of readiness for the SRcondition.

The software application guides the user (e.g., the behavior analyst)between the EO state and the SR state in this way until the assessmentsession is finished. When each individual assessment session iscompleted, the software application moves into a “generate summary”phase 915. If more sessions are to be conducted, the user presses the“Next Session” button 831 and the software application returns to the SRphase 905 for the next session. When all sessions are competed, the userpresses the END button 833 and the software application moves into theend phase 917.

In order to collect training data, a pilot study was conducted with 7children with ADS aged 10 to 15 years old (6 boys, 1 girl; meanage=12.20 years, SD=1.37). The protocol was reviewed and approved by theInstitutional Review Board (IRB) at Vanderbilt University. The researchteam members explained the purpose, protocols, and any potential risksof the experiment to both the parents and the children and answered alltheir question before seeking informed consent from the parents andinformed assents from the children. Because the purpose of the study wasto evoke and respond to precursor to problem behaviors and preventescalation to dangerous problem behaviors, parents and a dedicatedbehavior analyst data collector observed the assessment sessions toensure that all precursors and problem behaviors as well as the targetaffect of happy, relaxed, and engaged were correctly recorded. Allparticipants completed all of their experiments.

The pilot study was conducted in a child-proof room having twocompartments: an experimental space and an observation space. The childsits in the experimental space with a BCBA (Board certified behavioralanalyst). The seat for children is 2 meters away from the Kinect and avideo camera. The child wears an E4 sensor on the non-dominant wrist andthe body motion sensor system of FIG. 4 on the upper body. Fourobservers including an engineer, one of the parents, a behavior datacollector and a behavior assessment manager are seated in theobservation space, which has a one-way mirror towards the experimentalspace. The observers and the parent can see the therapist and the child,but not the other way around.

The child is first invited to the experimental space by the therapist.Then we close the door between the two spaces. The therapist puts the E4sensor on the wrist of the child and helps him or her wear the bodymotion sensor garment 501. Meanwhile, the parent and the other observersenter the observation room. Afterwards, the Kinect calibration isperformed when the therapist is out of the view of the Kinect.Therefore, the Kinect recognizes the body ID of the child and onlytracks data from the child. The therapist will be recognized as adifferent body ID and therefore does not interfere with the datacollection. Each experiment lasted for approximately one hour.

The experiment followed a modified version of the IISCA protocol. Weconducted multiple therapeutic sessions in a single experiment visit tocapture data on different behavioral states. These sessions are labeledas control (C) and test (T). The sessions are structured as CTCTT, whichis a classical design for single subject research. The control sessionscontain only SR conditions and the test sessions alternate between EOand SR presentations. EO is followed by SR and EO is applied once againafter at least 30 seconds have elapsed during which the children staycalm. During EO presentations, the therapist simulates the antecedentconditions that were most likely to evoke precursors and problembehaviors. These tasks were reported by parents in the open-endedinterview and include asking them to complete homework assignments,withdrawing preferred attention, and removing preferred toys orelectronics. During SR condition presentations, the therapist offersfree access to their favorite toys and electronics, removes all demandsof letting them work and the work-related materials, and provides themwith the kinds of attention reported to be preferred. Parents of thechildren observed from behind the one-sided mirror, watch the behaviorsof the child, and give feedback to the data collector and manager whoverified the occurrence of precursors or problem behaviors and thepresence or absence of a calm state.

All 7 children completed the entire experiments. The average duration ofeach experimental session was 54.2 minutes (min=36.5 minutes, max=63.1minutes, SD=11.5 minutes), with time variability across children largelydue to differences in how long it took for each child to calm downduring SR sessions. The body motion sensor garment is the most invasivecomponent in the platform; 6 out of 7 children tolerated it withoutproblem. Some children even put garment 501 on themselves. The onlychild who did not tolerate the garment 501 the entire time put it on atthe beginning and then decided to take it off after 15 minutes becausehe was very touch sensitive.

The other platform component that one had to wear, the E4 wristband, wasless invasive and tolerated well by all children. With regard to stayingwithin the view of the Kinect, 1 child was unable to stay seated at thetable throughout the entire experiment and instead spent some time onthe floor with toys and thus the Kinect was not able to track the childfor the entire duration of the experiment.

Multi-modal data was collected data using the systems and methodsdescribed above. Movement data collected at a sampling rate of 15 Hz.Raw signals for accelerations contain sensing noises and so a low-passfilter was applied with a cut-off frequency of 10 Hz. The threshold waschosen according to the usual speed of human motions so that the noiseswere filtered out while keeping information-rich signals for analysis.Peripheral physiological data including BVP and EDA were collected withthe sampling rates of 64 Hz and 4 Hz, respectively. BVP signals werefiltered by a 1 Hz to 8 Hz band pass filter. The EDA signals were notfiltered. There were some missing data entries in the head rotations andfacial expressions modalities. An interpolation algorithm was used tofill the sparse data. For the missing data points, we assign thenumerical mean value of the 20 closest available head rotations and themost frequent class among the 20 closest available facial expressions,respectively.

From the processed and filtered data, different features were selectedand extracted. Pitch, roll and yaw angles were computed as shown in theequations (1)-(3), above, to construct the upper body gestures. Certainpatterns of gestures are significantly related to problem behaviors andgestures are an important indicator for it. Common gestures observedduring problem behaviors include fist throwing, upper body swinging,laying back and repetitive arm movements. Besides gestures, averagemagnitude of accelerations of each joint can also be used as a measureof activity level as shown in equation (6). The activity level indicatesthe intensity of physical movements of the children. Fast and repetitivemovements such as throwing fist and fidgeting are a common category ofproblem behaviors and these movements have increased activity level.

$\begin{matrix}{{AL} = \frac{\sum\limits_{i = 1}^{n}\sqrt{a_{x_{i}}^{2} + a_{y_{i}}^{2} + a_{z_{i}}^{2}}}{7}} & (6)\end{matrix}$

Peripheral physiological data is a strong indicator of stress andseveral features including heart rate (HR) and skin conductance havebeen shown to have correlations with problem behaviors. Thus, wecomputed HR by inter-beat-intervals (IBI) of the BVP signal. From theEDA data, we separated and characterized the data into two types, whichwere tonic skin conductance level (SCL) and phasic skin conductanceresponse (SCR). These features carry information about the stress levelof a person.

Head banging is a very frequent problem behaviors reported for childrenwith ASD who exhibit problem behaviors. Measures of head rotations canbe used to predict head banging. Thus roll, pitch and yaw angles of thehead were chosen a features. From facial expressions, we extractedfeatures of closing of eyes and mouths, engagement, looking away andhappy. Children often show their frustration and anger on their facesand facial expressions can help locate more insidious precursors.

Once all the features were extracted, we then synchronized the differentdata modalities with varying updating frequencies. Motion data modalityhad the highest updating frequency and we interpolated other featuresinto each motion data interval to form multimodal data entries. First,the algorithm catches the next motion data entry. Secondly, thealgorithm computes the relative time from the beginning of theexperiment by its time stamp. Thirdly, the algorithm catches the mostrecent available values on other data modalities before that relativetime point. Finally, the algorithm interpolates these values into themotion data entry. In this way, each of the multimodal data entry hasall the features synchronized.

The software application of FIGS. 8 and 9 provides the time stamps ofprecursors of problem behaviors captured by the observers. With thesetime stamps, we can assign classes to each multimodal data entry betweenabsence and presence of imminent precursor. With the insight from ourIISCA practitioners we chose the data from 90 seconds prior to theepisode of precursor to the point of precursor generation to be the mostrepresentative for precursors of problem behaviors. Thus, for themultimodal data entry, if the data entry was collected within 90 secondsprior to the precursor we assigned it the label 1. Otherwise, the classwas assigned label 0. The two classes 0 and 1 have an average ratio of6:4. For our experiment, each child had an average of 27242 samples.

In this experiment, individualized models were trained (based on datafor each individual subject) and group models were also trained (basedon aggregated data for all subjects). The individualized models werebuilt with data of each child to adapt personal behavioral characterswith better accuracy. The group models were built with data from all thechildren to explore the general group behavioral pattern. In order tofind the most accurate ML algorithm, we explored several standard MLalgorithms with our datasets. We used the library scikit-learn onJupyter Notebook. The samples were randomly divided into training andtest sets with a ratio of 80 to 20. Then we ran a 5-fold crossvalidation to compute the accuracies of each algorithm.

For individualized models, Random Forest (RF), k Nearest Neighbors(kNN), Decision Tree (DT) and Neural Network (NN) were found to havehigh prediction accuracies while Support Vector Machine (SVM),Discriminant Analysis (DA) and Naive Bayes (NB) have comparatively loweraccuracies. For group models, RF, kNN, and DT also show high accuracieswhile DA has significantly lower accuracy. SVM, NB and NN have higherprediction accuracies in group models as compared to individual models.Group model has the samples from all participants combined and theincreased sample size would help the prediction to be more precise, butit may lose track of data pattern of individualized behaviors. Based onour observation, the problem behaviors of each child can differsignificantly. This trade-off may be the reason that some models stillhave high accuracies for group models or even better performs whileothers have lower accuracies. The RF individualized model has the bestaverage prediction accuracy.

The RF algorithm offers estimates of importance of each feature. Thefeature importance is computed as the total decrease in node impurityweighted by the probability of reaching that node. We also analyzed therelative importance of motion-based, physiology-based, and facialexpression-based features. The features included were motion signals ofeach joint, physiological signals, head rotations and facialexpressions. The results are consistent with the experimentalobservations. Main types of problem behaviors include the banging oftorso and movement of right arm is more informative because it is thedominant side. Head Rotations has an importance of 0.0689 andPhysiological data has an importance of 0.0331 including both HR andEDA. The Facial Expressions modality only has an importance of 0.0023.

We also analyzed the performance of the prediction algorithm when onlymotion-based and when only physiology-based features were used. We usedthe best performing algorithm RF to learn the data pattern of eachchild. The average prediction accuracies for the multimodal model,physiological data only model and motion data only model were 98.16%,86.88% and 91.63%, respectively.

As mentioned earlier, we assigned the class of imminent precursor whenthe data was collected within the last 90 seconds prior to the observedprecursor. To analyze the effect of the time window on the prediction ofprecursors, we varied the time window for the class of imminentprecursors from 30 seconds prior to the observed precursor to 120seconds in steps of 30 seconds. To avoid effects of different ratio ofclasses, we resample the minor class so that the two classes have a 1:1ratio. Additionally, during the experiments, we observed that some ofthe problem behaviors needed some time to cool down after the observedprecursors because it took some time for our therapists to intervene andfor the child to calm down. Therefore, we also explored assigning theclass of imminent precursor during a 10 seconds delay time after theobserved precursor. As shown in FIG. 16 , prediction accuracies for 30,60, 90 seconds do not have significant differences but it significantlydecreases for the 120 second time window. Also the models with the cooldown window of 10 seconds had an average prediction accuracy increase of0.06%. This analysis validates that the 90 seconds window seems to bethe optimal window for precursor prediction that has good predictionaccuracy with ample time for the caregivers to intervene.

Accordingly, the methods and system described herein provide, amongother things, mechanisms for automatically detect precursor conditionsfor problem behavior based on multimodal data signals prior to theoccurrence of the problem behavior using a trained machine-learningmodel. Other features and advantages of the invention are set forth inthe following claims.

What is claimed is:
 1. A system for predicting problem behavior inindividuals with developmental and behavior disabilities, the systemcomprising: a wearable upper body motion sensing device including aplurality of inertial measurement units positions at known positionedrelative to each other; and an electronic controller configured toreceive output signals from each of the plurality of inertialmeasurement units while the wearable upper body motion sensing device isworn by a subject individual, model an upper body position of thesubject individual based on the output signals from the plurality ofinertial measurement units, apply a trained machine-learning model byproviding, as input to the trained machine-learning model, an input dataset that includes at least one selected from a group consisting ofsignal data from at least one inertial measurement unit, the upper bodyposition information, and a sequence of upper body position information,wherein the trained machine-learning model is trained to produce asoutput, in response to receiving the input data set, an indication ofwhether a precursor to the problem behavior is detected, and generate anotification indicating that the precursor to the problem behavior isdetected in response to the trained machine-learning model producing anoutput indicating that the precursor to the problem behavior has beendetected.
 2. The system of claim 1, wherein the wearable upper bodymotion sensing device further includes a wearable upper body garment,wherein each of the plurality of inertial measurement units is affixedto the wearable upper body garments at positions such that, when thewearable upper body garment is worn by the subject individual theplurality of inertial measurement units includes a first inertialmeasurement unit positioned on a right forearm of the subjectindividual, a second inertial measurement unit positioned on a leftforearm of the subject individual, a third inertial measurement unitpositioned on a right upper arm of the subject individual, a fourthinertial measurement unit positioned on a left upper arm of the subjectindividual, and a fifth inertial measurement unit positioned on a backof the subject individual.
 3. The system of claim 2, wherein thewearable upper body garment includes at least one selected from a groupconsisting of a shirt, a crewneck sweatshirt, and a hooded sweatshirt.4. The system of claim 3, wherein the wearable upper body garmentincludes the hooded sweatshirt and wherein cabling and electroniccircuitry for the wearable upper body motion sensing device ispositioned within a hood of the hooded sweatshirt.
 5. The system ofclaim 2, wherein the wearable upper body garment includes a plurality ofpockets positioned at different locations on the wearable upper bodygarment, wherein each pocket of the plurality of pockets is sized toreceive a different individual inertial measurement unit of theplurality of inertial measurement units.
 6. The system of claim 5,wherein each pocket of the plurality of pocket is selectively closeableby a hook-and-loop closure material.
 7. The system of claim 1, whereinthe system further includes at least one physiological sensor, whereinthe electronic controller if further configured to receive outputsignals from the at least one physiological sensor, and wherein theelectronic controller is configured to applying the trainedmachine-learning model by providing, as input to the trainedmachine-learning model, an input data set that further includes at leastone selected from a group consisting of signal data from the at leastone physiological sensor and biometric feature metrics extracted fromthe signal data from the at least one physiological sensor.
 8. Thesystem of claim 7, wherein the electronic controller is furtherconfigured to extract at least one biometric feature from the signaldata from the at least one physiological sensor, the least one biometricfeature being selected from a group consisting of a bioimpedance, aheart rate, a skin conductance, and a body temperature.
 9. The system ofclaim 7, further comprising a wrist worn device including the at leastone physiological sensor, wherein the wrist worn device has anappearance of a wristwatch.
 10. The system of claim 1, furthercomprising a microphone, wherein the electronic controller is configuredto receive output signals from the microphone and to extract at leastone audio feature from the audio data, the at least one audio featurebeing selected from a group consisting of individual words, individualphrases, a change in verbal pitch of the subject individual, and achange in verbal volume of the subject individual, and wherein theelectronic controller is configured to applying the trainedmachine-learning model by providing, as the input to the trainedmachine-learning model, an input data set that further includes at leastone selected from a group consisting of the audio data and the at leastone audio feature extracted from the audio data.
 11. The system of claim1, wherein the electronic controller is configured to generate thenotification indicating that the precursor to the problem behavior isdetected by causing a portable device associated with at least oneindividual to output the notification, wherein the at least oneindividual is selected from a group consisting of the subjectindividual, a parent of the subject individual, a caregiver of thesubject individual, a teacher of the subject individual, and a therapistof the subject individual.
 12. The system of claim 1, wherein theelectronic controller is configured to generate the notificationindicating that the precursor to the problem behavior is detected bycausing a portable device associated with the subject individual thenotification indicating that the precursor has been detected andidentifying a recommended mitigation to prevent the problem behaviorfrom occurring.
 13. The system of claim 1, wherein the electroniccontroller is further configured to receive a first user inputindicative of an occurrence of a false positive and a second user inputindicative of an occurrence of a false negative, wherein the first userinput indicates that the machine-learning model determined that aprecursor had been detected when not precursor or problem behavior wasobserved by the user, wherein the second user input indicates that themachine learning model did not determine that the precursor had beendetected when precursor or problem behavior was observed by the user,and retrain the machine-learning model by determining a ground truthlabel based on the first user input or the second user input and mappingthe new ground truth label to signal data from plurality of inertialmeasurement units captured at a same time as the first user input or thesecond user input is received.
 14. A method of training themachine-learning model used in the system of claim 1, the methodcomprising: equipping the subject individual with the wearable upperbody motion sensing device; administering a behavior assessment whilerecording signal data from the plurality of inertial measurement units,wherein administering the behavioral assessment includes a controlledsequence of actions in which the subject individual is exposed tostimulus that may provoke the problem behavior and the precursor;receiving, from a user through a computer-based user device, real-timeindications of observed occurrences of the problem behavior and observedoccurrences of the precursor while the behavior assessment isadministered; generating a set of training data by defining a pluralityof ground truth labels based on the received real-time indications ofthe observed occurrences of the problem behavior and the observedoccurrences of the precursor, and mapping each real-time indication torecorded signal data from the plurality of inertial measurement units;and training the machine-learning model to produce an outputcorresponding to the ground truth label in response to receiving asinput at least one selected from a group consisting of the signal datafrom at least one inertial measurement unit mapped to the ground truthlabel, upper body position information determined based on the signaldata mapped to the ground truth label, and a sequence of upper bodyposition information determined based on the signal data mapped to theground truth label.
 15. The method of claim 14, wherein administeringthe behavior assessment includes administering an interview-informedsynthesized contingency analysis (IISCA).
 16. The method of claim 14,wherein training the machine-learning model includes training anindividualized machine-learning model based only on ground truth labelsand recorded signal data for the subject individual.
 17. The method ofclaim 14, wherein training the machine-learning model includes traininga group machine-learning model based on aggregated ground truth labelsand recorded signal data for each of a plurality of subject individuals.